Frédéric Amblard

Biographic Data

ID259123
NAMEFrédéric Amblard
GIVEN NAMESFrédéric
FAMILY NAMEAmblard
SIGNATUREAMBLARD F
AFFILIATIONSUniversité Toulouse III - Paul Sabatier
ORCID0000-0002-2653-0857
VERIFIEDYes
TOTAL WORKS13
TOTAL CITATIONS29
AUTHOR COUNT11
EDITOR COUNT2
FIRST PUBLICATION YEAR2000
LATEST PUBLICATION YEAR2015
H-INDEX2

  • Advances in Artificial Economics

    Francisco J Miguel Quesada, Gaudou Benoit et al.BOOKAdvances in Artificial Economics2015

  • Dynamic Community Detection

    Open AccessRemy Cazabet, Frédéric AmblardCHAPTEREncyclopedia of Social Network…2014

  • Stability and Evolution of Scientific Networks

    Open AccessEugenia Galeota, Susanna Liberti et al.CHAPTEREncyclopedia of Social Network…2014

  • Climate Change on Twitter: Topics, Communities and Conversations about the 2013 IPCC Working Group 1 Report

    Open AccessWarren Pearce, Kim Holmberg et al.ARTICLEPLoS ONE2014

    In September 2013 the Intergovernmental Panel on Climate Change published its Working Group 1 report, the first comprehensive assessment of physical climate science in six years, constituting a critical event in the societal debate about climate change. This paper analyses the nature of this debate in one public forum: Twitter. Using statistical methods, tweets were analyzed to discover the hashtags used when people tweeted about the IPCC report,…

  • The Results of Meadows and Cliff Are Wrong Because They Compute Indicator y Before Model Convergence

    Open AccessGuillaume Deffuant, Gerard Weisbuch et al.ARTICLEJournal of Artificial Societies…2013

    Meadows and Cliff (2012) failed to replicate the results of Deffuant et al. (2002) and concluded that our paper was wrong. In this note, we show that the conclusions of Meadows and Cliff are due to a wrong computation of indicator y, which was not fully specified in our 2002 paper. In particular, Meadows and Cliff compute indicator y before model convergence whereas this indicator should be computed after model convergence

  • Using dynamic community detection to identify trends in user-generated content

    Open AccessRemy Cazabet, Hideaki Takeda et al.ARTICLESocial Network Analysis and Mining2012Cited by: 6References: 15

  • Selection in scientific networks

    Open AccessWalter Quattrociocchi, Frédéric Amblard et al.ARTICLESocial Network Analysis and Mining2012Cited by: 1References: 40

  • How can social network analysis improve the study of primate behavior

    Open AccessChristian Sueur, Cédric Sueur et al.ARTICLEAmerican Journal of Primatology2011

    When living in a group, individuals have to make trade‐offs, and compromise, in order to balance the advantages and disadvantages of group life. Strategies that enable individuals to achieve this typically affect inter‐individual interactions resulting in nonrandom associations. Studying the patterns of this assortativity using social network analyses can allow us to explore how individual behavior influences what happens at the group, or populat…

  • Construire des sociétés artificielles pour comprendre les phénomènes sociaux réels

    Open AccessFrédéric AmblardARTICLENouvelles perspectives en…2010

    Nous présentons ici rapidement l’approche de modélisation et de simulation multi-agent, ses principales caractéristiques ainsi que son intérêt pour les sciences sociales. En particulier, nous insistons sur la proximité de cette formalisation avec des cadres de pensées classiques en sciences sociales comme l’individualisme méthodologique et nous proposons un usage possible de cette approche comme outil permettant de formaliser et d’interroger les …

  • Guess You’re Right on This One Too: Central and Peripheral Processing in Attitude Changes in Large Populations

    Wander Jager, Frédéric AmblardCHAPTERAdvancing Social Simulation2008

  • An Individual-Based Model of Innovation Diffusion Mixing Social Value and Individual Benefit

    Guillaume Deffuant, Sylvie Huet et al.ARTICLEAmerican Journal of Sociology2005Cited by: 22References: 14

    The authors propose an individual‐based model of innovation diffusion and explore its main dynamical properties. In the model, individuals assign an a priori social value to an innovation which evolves during their interactions with the "relative agreement" influence model. This model offers the possibility of including a minority of "extremists" with extreme and very definite opinions. Individuals who give a high social value to the innovation t…

  • Interacting Agents and Continuous Opinions Dynamics

    Open AccessG Weisbuch, Guillaume Deffuant et al.CHAPTERHeterogenous Agents, Interactions…2003

  • Mixing beliefs among interacting agents

    Guillaume Deffuant, David Neau et al.ARTICLEAdvances in Complex Systems2000

    We present a model of opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. High thresholds yield convergence of opinions towards an average opinion, whereas low thresholds result in several opinion clusters: members of the same cluster share the same opinion but are no longer influenced by members of other clusters.